# How the parallelization works in the engine¶

The engine builds on top of existing parallelization libraries. Specifically, on a single machine it is based on multiprocessing, which is part of the Python standard library, while on a cluster it is based on the combination celery/rabbitmq, which are well-known and maintained tools.

While the parallelization used by the engine may look trivial in theory (it only addresses embarrassingly parallel problems, not true concurrency) in practice it is far from being so. For instance a crucial feature that the GEM staff requires is the ability to kill (revoke) a running calculation without affecting other calculations that may be running concurrently.

Because of this requirement, we abandoned concurrent.futures, which is also in the standard library, but is lacking the ability to kill the pool of processes, which is instead available in multiprocessing with the Pool.shutdown method. For the same reason, we discarded dask, which is a lot more powerful than celery but lacks the revoke functionality.

Using a real cluster scheduling mechanism (like SLURM) would be of course better, but we do not want to impose on our users a specific cluster architecture. celery/rabbitmq have the advantage of being simple to install and manage. Still, the architecture of the engine parallelization library is such that it is very simple to replace celery/rabbitmq with other parallelization mechanisms: people interested in doing so should just contact us.

Another tricky aspects of parallelizing large scientific calculations is that the amount of data returned can exceed the 4 GB limit of Python pickles: in this case one gets ugly runtime errors. The solution we found is to make it possible to yield partial results from a task: in this way instead of returning say 40 GB from one task, one can yield 40 times partial results of 1 GB each, thus bypassing the 4 GB limit. It is up to the implementor to code the task carefully. In order to do so, it is essential to have in place some monitoring mechanism measuring how much data is returned back from a task, as well as other essential informations like how much memory is allocated and how long it takes to run a task.

To this aim the OpenQuake engine offers a Monitor class (located in openquake.baselib.performance) which is perfectly well integrated with the parallelization framework, so much that every task gets a Monitor object, a context manager that can be used to measure time and memory of specific parts of a task. Moreover, the monitor automatically measures time and memory for the whole task, as well as the size of the returned output (or outputs). Such information is stored in an HDF5 file that you must pass to the monitor when instantiating it. The engine automatically does that for you by passing the pathname of the datastore.

In OpenQuake a task is just a Python function (or generator) with positional arguments, where the last argument is a Monitor instance. For instance the rupture generator task in an event based calculation is coded more or less like this:

def sample_ruptures(sources, num_samples, monitor):  # simplified code
ebruptures = []
for src in sources:
for rup, n_occ in src.sample_ruptures(num_samples):
ebr = EBRupture(rup, src.id, grp_id, n_occ)
eb_ruptures.append(ebr)
if len(eb_ruptures) > MAX_RUPTURES:
# yield partial result to avoid running out of memory
yield eb_ruptures
eb_ruptures.clear()
if ebruptures:
yield eb_ruptures


If you know that there is no risk of running out of memory and/or passing the pickle limit you can just use a regular function and return a single result instead of yielding partial results. This is the case when computing the hazard curves, because the algorithm is considering one rupture at the time and it is not accumulating ruptures in memory, differently from what happens when sampling the ruptures in event based.

If you have ever coded in celery, you will see that the OpenQuake engine concept of task is different: there is no @task decorator and while at the end engine tasks will become celery tasks this is hidden to the developer. The reason is that we needed a celery-independent abstraction layer to make it possible to use different kinds of parallelization frameworks/

From the point of view of the coder, in the engine there is no difference between a task running on a cluster using celery and a task running locally using multiprocessing.Pool: they are coded the same, but depending on a configuration parameter in openquake.cfg (distribute=celery or distribute=processpool) the engine will treat them differently. You can also set an environment variable OQ_DISTRIBUTE, which takes the precedence over openquake.cfg, to specify which kind of distribution you want to use (celery or processpool): this is mostly used when debugging, when you typically insert a breakpoint in the task and then run the calculation with

\$ OQ_DISTRIBUTE=no oq run job.ini


no is a perfectly valid distribution mechanism in which there is actually no distribution and all the tasks run sequentially in the same core. Having this functionality is invaluable for debugging.

Another tricky bit of real life parallelism in Python is that forking does not play well with the HDF5 library: so in the engine we are using multiprocessing in the spawn mode, not in fork mode: fortunately this feature has become available to us in Python 3 and it made our life a lot happier. Before it was extremely easy to incur unspecified behavior, meaning that reading an HDF5 file from a forked process could

1. work perfectly well
3. cause a segmentation fault

and all of the three things could happen unpredictably at any moment, depending on the machine where the calculation was running, the load on the machine, and any kind of environmental circumstances.

Also, while in theory with the newest HDF5 libraries it should be possible to use a SWMR architecture (Single Writer Multiple Reader) we were not able to get this working in the engine. Instead, we are using a two files approach which is simple and works very well: we read from one file (with multiple readers) and we write on the other file (with a single writer), instead of reading/writing on the same file. This bypasses all the limitations of the SWMR mode in HDF5 and did not require a large refactoring of our existing code.

Another tricky point in cluster situations is that rabbitmq is not good at transferring gigabytes of data: it was meant to manage lots of small messages, but here we are perverting it to manage huge messages, i.e. the large arrays coming from a scientific calculations.

## How to use openquake.baselib.parallel¶

Suppose you want to code a character-counting algorithm, which is a textbook exercise in parallel computing and suppose that you want to store information about the performance of the algorithm. Then you should use the OpenQuake Monitor class, as well as the utility openquake.baselib.datastore.hdf5new that build an empty datastore for you. Having done that, the openquake.baselib.parallel.Starmap class can take care of the parallelization for you as in the following example:

import os
import sys
import pathlib
import collections
from openquake.baselib.performance import Monitor
from openquake.baselib.parallel import Starmap
from openquake.baselib.datastore import hdf5new

def count(text):
c = collections.Counter()
for word in text.split():
c += collections.Counter(word)
return c

def main(dirname):
dname = pathlib.Path(dirname)
with hdf5new() as hdf5:  # create a new datastore
monitor = Monitor('count', hdf5)  # create a new monitor
for fname in os.listdir(dname)
if fname.endswith('.rst'))  # read the docs
c = collections.Counter()  # intially empty counter
for counter in Starmap(count, iterargs, monitor):
c += counter
print(c)  # total counts
print('Performance info stored in', hdf5)

if __name__ == '__main__':
main(sys.argv[1])  # pass the directory where the .rst files are


The name Starmap was chosen it looks very similar to multiprocessing.Pool.starmap works, the only apparent difference being in the additional monitor argument:

pool.starmap(func, iterargs) ->  Starmap(func, iterargs, monitor)


In reality the Starmap has a few other differences:

1. it does not use the multiprocessing mechanism to returns back the results, it uses zmq instead;
2. thanks to that, it can be extended to generator functions and can yield partial results, thus overcoming the limitations of multiprocessing
3. the Starmap has a .submit method and it is actually more similar to concurrent.futures than to multiprocessing.

Here is how you would write the same example by using .submit:

def main(dirname):
dname = pathlib.Path(dirname)
with hdf5new() as hdf5:
smap = Starmap(count, monitor=Monitor('count', hdf5))
for fname in os.listdir(dname):
if fname.endswith('.rst'):

The difference with concurrent.futures is that the Starmap takes care for of all submitted tasks, so you do not need to use something like concurrent.futures.completed, you can just loop on the Starmap object to get the results from the various tasks.
The .submit approach is more general: for instance you could define more than one Starmap object at the same time and submit some tasks with a starmap and some others with another starmap: this may help parallelizing complex situations where it is expensive to use a single starmap. However, there is limit on the number of starmaps that can be alive at the same moment.
Moreover the Starmap has a .shutdown methods that allows to shutdown the underlying pool.
The idea is to submit the text of each file - here I am considering .rst files, like the ones composing this manual - and then loop over the results of the Starmap. This is very similar to how concurrent.futures works.